SCAPS-1D simulation of lead-free Cs<sub>2</sub>TiBr<sub>6</sub> based perovskite solar cells: the impact of electron and hole transport layers
Bibliographic record
Abstract
Perovskite solar cells (PSCs) have garnered significant attention due to their remarkable power conversion efficiency, yet concerns surrounding the stability and toxicity of lead halide perovskites have hindered their full-scale commercialization. In addressing this dilemma which arise in part from energy band misalignment, this study employs computational modeling to investigate Cs 2 TiBr 6 PSCs connected through various hole transport layers (HTLs) and electron transport layers (ETLs). Furthermore, a comprehensive investigation of parameters such as the absorber layer thickness, defect density, operating temperature, and rear metal electrode configuration has been conducted to optimize the photovoltaic performance. Through systematic optimization, a promising configuration comprising the layer sequence Glass/FTO/ZnO/Cs 2 TiBr 6 /CZTSe/Au has been identified, yielding a potential power conversion efficiency of 20.40%. Additionally, an alternative configuration, Glass/FTO/ZnO/Cs 2 TiBr 6 /CZTSe/Pt, has suggested a further enhancement in power conversion efficiency up to 30.37%. Notably, this configuration yields the highest V oc of 1.21 V, Jsc of 28.24 mA/cm², and FF of 89.12%, resulting in an impressive PCE of 30.61% when selenium (Se) is used instead of Platinum (Pt) as the back contact material. The exceptional performance of the CZTSe HTL is attributed to the quantum efficiency (QE) of the device, which indicates a proper alignment of its energy bands with those of the double perovskite absorber layer. This synergetic alignment is deemed responsible for improved efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".